Results for “sUAS” — Page 11

NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗

NASA NTRS2019

Identification and Prediction of Broadband Noise for a Small Quadcopter

The growing interest in the noise of small unmanned aircraft systems (sUAS) in operation has motivated this study to characterize and predict broadband rotor noise sources. This paper analyzes the performance and acoustic data collected from two sets of wind tunnel experiments for hover and forward flight operating conditions. The first set of data is a result of testing a representative vehicle configuration of the Straight Up Imaging (SUI) Endurance quadcopter, while the second set of data represents an isolated rotor from the same vehicle. Following the analysis of the empirical data, a broadband noise prediction methodology is employed to compare with the experiments. This methodology uses NASA’s ROTONET and BARC tools to predict blade loading and self-noise at conditions matching the wind tunnel experiments. This methodology is effective at predicting the broadband noise at certain conditions. Furthermore, some predictions are repeated with the inflow conditions calculated by the rotorcraft analysis tool CAMRAD II, which shows improved prediction results for cases with nonuniform inflow. This work confirms some of the unique challenges associated with testing small rotor configurations, explores the limitations of this prediction methodology, and suggests improvements that can be made for future studies.

Nicole A Pettingill↗

NASA NTRSDate not supplied

SWS Project Relevant Research

Project Overview Tech Challenge 2 - In-Time Safety Management for Emerging Operations - (2018-19) Initial Architecture and Information Req'ts - UTM-like ecosystem; ASIAS/SWIM-like info sharing - Ref: NASA TM-2020-220440 - (2020-2021) Phase 1 - Architecture and SFCs tailored to domain risks - Build collaborations on key topics - (2021-2022) Phase 2 - Services with infrastructure for urban ops - Broader in-time risk assessment span - (2023-2024) Phase 3 - With and by partners - Urban sUAS and AAM/UAM domains

Wind modeling↗

NASA NTRSDate not supplied

MPATH (Measuring Performance for Autonomy Teaming with Humans) Ground Control Station: Design Approach and Initial Usability Results

Envisioned future Advanced Air Mobility (AAM) operations will require a transition of aircraft command and control from onboard pilots to remote operators. The National Aeronautics and Space Administration (NASA) has developed a research ground control station (GCS) software called MPATH (Measuring Performance for Autonomy Teaming with Humans) to study the human factors of remote operators in a representative AAM environment, where small uncrewed aerial systems (sUAS; simulated or real) act as surrogates for larger AAM aircraft. A primary focus of the research being conducted with the MPATH GCS is scalability (i.e., one human managing multiple vehicles). MPATH has demonstrated to be a useful capability for human factors research and remote operations. Two initial usability studies and a multi-vehicle control assessment were recently conducted, and usability data and operator feedback were collected. Generally, participants rated MPATH high on usability and interface quality, whereas information quality was rated slightly lower. These results were supported by specific feedback. Several generalized GCS design recommendations are proposed based on the results and feedback from these flight activities. Future updates to MPATH will incorporate the proposed recommendations. In practice, these recommendations could be used by any GCS software designer or developer to promote usability and safety.

Advanced Air Mobility↗

NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗

NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗

NASA NTRS2023

m:N Working Group Spring Meeting Summary

On May 9th and 11th, 2023, the NASA-led MultiVehicle (m:N) Working Group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Pseudo Satellite (HAPS), and Urban Air Mobility (UAM)] met in Denver, CO at XPONENTIAL 2023, co-hosted by the Association for Uncrewed Vehicle Systems International (AUVSI) and Messe Düsseldorf North America (MDNA), for an in-person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year, however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N working group is co-chaired by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups, including the FAA and RTCA’s SC-228 Detect and Avoid.

multi-vehicle↗

NASA NTRSDate not supplied

Usability of Pre-Flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗

NASA NTRS2022

m:N Working Group

On November 29th and 30th, 2022, the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Ames Research Center in Mountain View, CA for an in person meeting. The option to dial in remotely and use Conference.IO to engage with questions was offered as well. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA and RTCA’s SC-228 Detect and Avoid. Each subgroup is run by a government/industry team.

multi-vehicle↗

NASA NTRSDate not supplied

Usability of Pre-flight Planning Interfaces for Supplemental Data Service Provider Tools to Support Uncrewed Aircraft System Traffic Management

Small uncrewed aircraft systems (sUASs) operate in low-altitude, uncontrolled airspace – where support services for their operators (UASOs) are not currently provided. NASA’s System-Wide Safety (SWS) project is identifying the potential risks and hazards to sUAS operations to provide, inform, and improve the designs of In-time Aviation Safety Management Systems (IASMS). The IASMS will include a suite of data-driven tools that compile and analyze data collected from aviation systems and environmental sources to predict hazards, and provide information to allow operators to mitigate these risks (Young et al., 2020). These risk and hazard services can be run and displayed to operators on graphical user interfaces (GUIs), as they relate to a vehicle(s)’ route of flight. These interfaces offer both a means to present hazard service output and offer an opportunity to test user understanding of the information, user decision making, and the best ways to present such data to an operator. Based on these future technologies and intended missions, it is important to investigate interface requirements and evaluate how operators might use these tools. Presenting salient and meaningful risk assessment information to operators is necessary to increase situation awareness and ultimately safety. Building on previous research (Feldman et al., 2022), a usability study comparing two GUIs was conducted to explore how individuals interacted with different styles of information displays. A series of pre-flight hazard and risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider Consolidated Dashboard and the Human Automation Team Interface System interfaces. Participants were trained to use both GUIs and their performance was analysed across different scenarios involving multiple sUASs. Performance on simple tasks and the System Usability Scale scores were reported by Feldman et al., 2023. Additional analyses and evaluations on more complex tasks (e.g., risk assessment, prioritization), workload and response times are examined in this paper.

sUAV interfaces↗

NASA NTRS2023

m:N Working Group Annual Status Report

This document serves as an annual report of m:N Unmanned Aircraft Systems (UAS) subgroup activities, addressed challenges, and roadmaps for the future. The subgroups consist of small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM). Also included in this report are participant lists for each subgroup (Appendix B) and future roadmap and outreach plans.The subgroups meet in a virtual format multiple times throughout the year. Twice a year, participants from all the subgroups come together as part of the m:N working group to brief each other in person on progress, challenges, and path forward ideas for successful incorporation of UAS into the airspace. Recent m:N in person meetings include: - November 29-30, 2022 at the NASA Ames Research Center in Mountain View, CA - May 9 & 11, 2023 at AUVSI’s Xponential conference in Denver, CO - The next in person working group meeting is planned for November 28-30, 2023 at NASA Langley in Hampton, Virginia The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the Federal Aviation Administration (FAA) and Radio Technical Commission for Aeronautics (RTCA’s) SC-228 Detect and Avoid committee.

multi-vehicle↗

NASA NTRSDate not supplied

Visual and Inertial Datasets for an eVTOL Aircraft Approach and Landing Scenario

A National Aeronautics and Space Administration (NASA) project developing computer vision algorithms for autonomous flight is producing real-world datasets with cameras mounted on aircraft. In related domains, such as autonomous driving, open datasets are key to innovation and advancement in computer vision and autonomous perception for future Advanced Air Mobility (AAM) operations. Few vision datasets, however, are publicly available in the aviation context. This paper introduces preliminary datasets containing several examples of approach and landing scenarios. The platform aircraft include a multirotor small unmanned aerial system (sUAS) and a crewed helicopter as surrogates for future electric vertical take-off and landing (eVTOL) aircraft. The dataset provides video imagery with associated inertial navigation system-global positioning system (INS-GPS) position and attitude estimates and other sensors. Surveyed locations of the visual features of the landing area are included. This dataset is the first to be released in an ongoing effort to collect and share large, diverse datasets relevant to autonomous aviation; community critique that can inform and improve future flight campaigns is welcome.

Nelson Brown↗

NASA NTRSDate not supplied

MBSE Execution of Scalable Autonomous Operations for a High Density Vertiplex

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) and NASA SE processes were executed via MBSE using MagicDraw. Since the adoption of MBSE and utilization of MagicDraw, the systems engineering team has made tremendous strides in each pillar of MBSE including, requirements, behavior, and structure. Scalable Autonomous Operations (SAO) was a stage in the development of the High Density Vertiplex focusing on the autonomous terminal operations of a vertiport with sUAS aircraft. MBSE served the systems engineering team to document and verify the physical architecture and capture a logical architecture of SAO for distribution to the AAM community. This paper will detail methodologies that were created to successfully execute NASA SE processes via MBSE in the SAO stage as well as highlight challenges and lessons learned.

Demetrios Katsaduros↗

NASA NTRS2024

Assessment of Secondary Pitot Probe Locations on a Small UAS

Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) have been at the forefront of many aeronautical discussions because of the potential revolutionary improvement to urban transportation. In this study, a representative Research Aircraft for eVTOL Enabling techNologies, Sub-scale Wind Tunnel Flight Test (RAVEN SWFT) nosecone was placed inside the NASA Langley Research Center’s 12-Foot Low-Speed Wind Tunnel with a pitot-static assembly installed. The study aimed to determine a suitable location and orientation of the pitot-static assembly on the nose of the vehicle, which consists of two airspeed sensors: one for the flight controller and one for data collection. The dynamic pressure was varied from 0.5 to 5psf for each test point. Additionally, three different offset distances from the center boom and three different angles for the flight controller pitot tube were tested. The data from both of the pitot tube assemblies were compared to the wind tunnel and the Aeroprobe pitot tube. The results show the flight controller pitot tube performs better when it is placed further away from the nosecone, regardless of the angle. The results also show that the angle does not affect the accuracy of the pitot tube when the vehicle yaws or pitches regardless of the mounting angle and location. The study provides useful insights for the design of the airspeed measurement systems on small Uncrewed Aerial Systems (sUAS), which can be applied to other UAM and AAM applications.

AAM↗

NASA NTRS2024

High Density Vertiplex - Scalable Autonomous Operations - Flight Test Report

This Technical Memorandum describes the approach taken within the High Density Vertiplex Project to perform rapid prototyping and assessment of the UAM Ecosystem including representative: Onboard Autonomous Systems, Ground Control and Fleet Management Systems, Airspace Management Systems, and Vertiport Automation Systems (VAS). Small Uncrewed Aerial Systems (sUAS) were employed as effective low risk and inexpensive surrogates for larger proposed UAM aircraft to accelerate the prototyping effort, ensure safety, greatly mitigate costs, and accelerate progress. Flight testing performed included multivehicle operations where usability Human Factors (HF) data was collected on the operators.

Jacob Schaefer↗

NASA NTRSDate not supplied

Lidar and UAS Measurements of Winds for the NASA Advanced Air Mobility Mission

The envisioned future of Advanced Air Mobility involves low-altitude operation of a new class of air vehicles. The low altitudes of the flight paths would have these vehicles spending most of their operation in conditions where unpredictable wind and turbulence effects may occur. Wind measurements will hence be critical to ensure safe and efficient operations for AAM. Such wind measurement could be used as a monitoring system for warning of hazardous wind events, as data input to forecasting models, or as a research tool to understand wind effects in complex environments. To meet this need NASA is evaluating wind sensing technologies with a capability to probe the atmospheric boundary layer. Doppler lidar is a leading candidate ground-based sensor, as shown by the decades-long history of being an effective tool for many wind studies. However, there is a need to re-assess the Doppler wind lidars for the AAM application which involves evaluating wind effects as vehicles operate in and out of vertiports, notably regarding spatial resolution. Meeting the needs for spatial resolution may involve the use of dual-Doppler techniques, rather than just single Doppler lidar. The study was furthermore motivated by looking toward the future of AAM, in which the air vehicles involved can also provide wind measurements. Airborne wind measurements, obtained directly from vehicle-mounted anemometers or indirectly from vehicle navigation data, offer a means to compare remotely sensed wind lidar with in-situ measurements. The following sections report on results of wind measurements obtained using lidar and the small uninhabited aircraft systems (sUAS’s) operating in the same volume of air.

Adam Medina↗

NASA NTRS2024

Quantification of Uncertainty and Risk Sensitivity for Safety of Emerging Operations

The growing need to develop and deploy small unmanned aerial vehicles (sUAVs) for various applications in the airspace necessitates reliable tools to accurately predict the flight trajectories of the sUAVs. The knowledge of the predicted trajectories help decision makers anticipate potential conflict, assess the risk, and take appropriate risk mitigation actions. In addition, uncertainties in vehicle models, weather, and controller action further highlights the need for reliable prediction tools. In this project, the application of mixed sparse grid-based quadrature and generalized polynomial chaos(gPC) expansion method for uncertainty quantification and collision assessment in air traffic consisting of fixed-wing small unmanned aerial vehicles (sUAV) was studied. From the results obtained, it can be concluded that this provides a reliable framework to carry out quantitative conflict assessment in an unmanned air traffic, which when employed, can improve the functionalities of the unmanned traffic management system. It was observed that the results from the gPC expansion framework developed in the project can be utilized to conduct rapid probabilistic collision assessment for near real-time unmanned traffic management in the airspace. From the vehicle models, position updates, and wind-field data, a priori gPC based 3-σcon-fidence ellipses can provide estimates of potential conflict at some future instants. The computational costs scaled linearly when the uncertain inputs were fewer. Further, the largest allowable distribution of para-metric uncertainties that leads to the smallest risk of collision in traffic of small unmanned aerial vehicles could be calculated. The time of closest approach between two sUAVs can be established paving way for development of proactive mitigation strategies. The separation between the sUAVs was found to be most significantly affected by uncertainties in the maximum available thrusts, zero-lift drag coefficients, and wing planform areas of the sUAVs. The study of uncertain wind-fields indicated that a heterogeneous traffic mix resulted in an increased probability of conflict. Increased measurement update rate reduced the uncertain-ties in the trajectories of the vehicles, further reducing the probability of conflict but rapid updates of all vehicles in the airspace poses a stringent communication limitation. The gPC framework also provided the means to analyze vehicle impact (crash region) due to loss of control resulting from actuator failure in sUAS traffic, essentially to predict impact and crash zones for representative vehicles. The predicted regions when compared with non-participant density, provides a means to develop an early mitigation strategy, should the sUAV detect an imminent actuator failure.

Rajnish Bhusal↗

NASA NTRS2025

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗